Question

Variance filtering prior to RNA-seq differential expression

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chris86 ▴ 420

@chris86-8408

Last seen 4.4 years ago

UCL, United Kingdom

Hi

I want to variance filter my RNA-seq data to increase statistical power similar to what I have seen done in microarray studies where the data groups are pretty similar. However I cannot use limma/deseq etc after the variance filter because it screws up the background variance, is it OK to do a t-test? I think there are problems with the normal distribution not fitting RNA-seq data, in which case, what to do? Any ideas?

Cheers.

sequencing deseq limma • 3.4k views

ADD COMMENT • link updated 8.8 years ago by Gordon Smyth 50k • written 8.8 years ago by chris86 ▴ 420

score 3 · Accepted Answer · 2015-07-20

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Gordon Smyth 50k

@gordon-smyth

Last seen 4 hours ago

WEHI, Melbourne, Australia

Don't do variance filtering -- then the problems you mention will all disappear.

Variance filtering is hardly ever a good idea, and almost certainly not with RNA-seq data.

ADD COMMENT • link 8.8 years ago Gordon Smyth 50k

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I' new to rna seq analysis. So pardon my ignorance.

Why is variance filtering hardly ever a good idea, and almost certainly not with RNA-seq data?

What if your downstream analysis is a clustering algorithm instead of differential expression?

Best

ADD REPLY • link 8.6 years ago nickbern92 • 0

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For clustering that is fine, it makes sense. For differential expression many of the packages do variance sharing to increase power so you cannot remove the low variance ones. Actually I compared variance filtering + t test with limma for finding DE genes, and limma came out on top because it does this information sharing.